Analysis
ABB
17 July, 2023
Plan of analysis to be carried out
for simple diagrams,
for complex connections - sankey diagram - for the link between printing methods + printing model with a link to the bioink type (+ origin)?
first things first, to give numbers of studies that report e.g. A or B
Printing Techniques
Give an overview of the different printing techniques, - method - printer model - printer source - forms - software
Kind of Printer Method
t1 <- table(reconciled$`4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`)
##
# Extrusion based Extrusion based|Unclear Inject based
# 37 1 4
# Other Stereolithography Unclear
# 9 2 10
# proportions instead of absolute value of number of studies
prop.table(table(reconciled$`4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`))##
## Extrusion based Extrusion based|Stereolithography
## 0.60317460 0.01587302
## Inject based Other
## 0.06349206 0.19047619
## Stereolithography Unclear
## 0.03174603 0.09523810 # barplot(t1, ylab = "Number of studies", ylim=c(0, max(t1) + 20),cex.names=.5 , col = "green")
text(x = barplot(t1, ylab = "Number of studies", ylim=c(0, max(t1) + 10), cex.names=.5 , col = "green", main = "Kind of printing method used"), y = t1 + 2, labels = t1, cex = 0.8)
### ABB add table to specfiy other
# -- link printing method with bioInk type - Reporting of Printer Model
#### Reporting of Printer Model
t2 <- table(reconciled$`4.1.1 Do the authors report the printer model name/number?_a3e72486-56d3-4154-a45f-9fb87b8612fc_Answer`)
# Not reported Not reported|Reported Reported
# 22 1 4
# barplot(table(reconciled$`4.1.1 Do the authors report the printer model name/number?_a3e72486-56d3-4154-a45f-9fb87b8612fc_Answer`), ylab = "Number of studies")
text(x = barplot(t2, ylab = "Number of studies", ylim=c(0, max(t2) + 10), cex.names=.5 , col = "green", main = "Is printer model name/number reported"), y = t2 + 2, labels = t2, cex = 0.8)
Printer Source
#### Reporting of Printer Source
t3 <- table(reconciled$`4.1.2 What is the source of the printer?_102ba965-5234-4b3e-8b23-bd70cf4e074d_Answer`)
# commercial modified commercial not reported
# 36 4 4
# self-made unclear unclear|commercial
# 13 5 1
# barplot(table(reconciled$`4.1.2 What is the source of the printer?_102ba965-5234-4b3e-8b23-bd70cf4e074d_Answer`), ylab = "Number of studies", cex.names=.5, col = "green")
text(x = barplot(t3, ylab = "Number of studies", ylim=c(0, max(t3) + 10), cex.names=.5 , col = "green", main = "Source of the printer"), y = t3 + 2, labels = t3, cex = 0.8)
Printer Forms
#### Reporting of Printer forms
# split is based on the ink
table(reconciled$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`)##
## Grid Grid;Other
## 19 1
## Grid|Grid lobular liver
## 2 7
## lobular liver;Other lobular liver|lobular liver
## 1 2
## Other Other|Other
## 25 5
## Toroids
## 1 # Grid Grid;Other
# 19 1
# Grid|Grid lobular liver
# 2 7
# lobular liver;Other lobular liver|lobular liver
# 1 2
# Other Other|Other
# 25 5
# Toroids
# 1
# ABB to clean these responses
# Those with a semicolon response ; should now be called "combo"
# responses separate with a pipe should be merged when they are saying the same thing.
printer_forms <- reconciled %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Grid|Grid" = "Grid")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "lobular liver|lobular liver" = "lobular liver")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Other|Other" = "Other")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Grid;Other" = "Combination")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "lobular liver;Other" = "Combination"))
t4 <-table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`)
# barplot(table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`), ylab = "Number of studies", cex.names=.5 , col = "green")
text(x = barplot(t4, ylab = "Number of studies", ylim=c(0, max(t4) + 10), cex.names=.5 , col = "green", main = "What kind of forms are printed with this ink"), y = t4 + 2, labels = t4, cex = 0.8)
Printer Forms Overtime
printer_form_year<- table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, printer_forms$NA_Year)
printer_form_yearDF <- as.data.frame(printer_form_year)
# long to wide
printer_form_yearDF <- printer_form_yearDF %>% spread(
key = Var2,
value = Freq)
# printer_form_yearDF
# names(printer_form_yearDF) <- gsub(x = names(printer_form_yearDF), pattern = "X", replacement = "")
library(formattable)
formattable(printer_form_yearDF,
#align =c("l", "r"),
list(
`Indicator Name` = formatter(
"span",
style = ~ style(color = "grey",font.weight = "bold")),
area(row = 1:5) ~ color_tile("white", "green")))## Warning in gradient(as.numeric(x), ...): NAs introduced by coercion| Var1 | 2010 | 2011 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Grid | 0 | 0 | 0 | 1 | 0 | 2 | 4 | 4 | 1 | 4 | 5 | 0 |
| Combination | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| lobular liver | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 2 | 2 | 1 |
| Other | 1 | 1 | 1 | 0 | 1 | 5 | 2 | 6 | 3 | 5 | 2 | 3 |
| Toroids | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Printer Software
#### Reporting of Printer software
table(reconciled$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`)##
## No No|No Not applicable Yes
## 39 1 1 22 # No No|No Not applicable Yes
# 39 1 1 22
## ABB combine No|No into the "No" group
printer_soft <- reconciled %>%
mutate(`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer` = recode(`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`, "No|No" = "No") )
t5 <- table(printer_soft$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`)
# barplot(table(printer_soft$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`), ylab = "Number of studies", cex.names=.5, col = "green")
text(x = barplot(t5, ylab = "Number of studies", ylim=c(0, max(t5) + 10), cex.names=.5 , col = "green", main = "Is the 3D modelling software reported"), y = t5 + 2, labels = t5, cex = 0.8)
## Slicing Software
# - since the review started, many cases - not applicable
# merge NA with No and merge No|No with NO
table(reconciled$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`)##
## No No|No Not applicable Yes
## 53 1 1 8 # No No|No Not applicable Yes
# 53 1 1 8
## ABB to clean No|No and Not applicable into the No option.
printer_slice <- printer_soft %>%
mutate(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer` = recode(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`, "No|No" = "No")) %>%
mutate(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer` = recode(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`, "Not applicable" = "No"))
t6 <- table(printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`)
# t6_6 <- table(printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`, printer_slice$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`)
# t6_6
# barplot(table(printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`), ylab = "Number of studies", cex.names=.5, col = "green")
text(x = barplot(t6, ylab = "Number of studies", ylim=c(0, max(t6) + 10), cex.names=.5 , col = "green", main = "Is the name of the slicing software reported?"), y = t6 + 2, labels = t6, cex = 0.8, )
#####
# stacked barplot to combine software & slice software
# library(ggplot2)
#
# software <- printer_slice %>%
# group_by(`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`, printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`) %>% count()
#
# colnames(software) <- c("threeD-software", "slice-software", "number-studies")
#
# #, printer_slice$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`))
#
# ggplot(software, aes(fill=`slice-software`, y=`number-studies`, x=`threeD-software`)) +
# geom_bar(position='stack', stat='identity')
#software$`number-studies`
############BioInks
Give an overview of the different inks used with these techniques. - type - origin - additives - density
ABB link bioink to printing method
## Type of BioInk
t7 <- table(reconciled$`4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`)
# clean?
# Natural Natural|Natural Natural|Synthetic
# 43 3 5
# Not reported Not reported|Natural Synthetic
# 2 1 4
# Unclear
# 5
# barplot(t7, ylab = "Number of studies", cex.names=.5, col = "blue")
text(x = barplot(t7, ylab = "Number of studies", ylim=c(0, max(t7) + 10), cex.names=.5 , col = "blue", main = "What type of BioInk is used"), y = t7 + 2, labels = t7, cex = 0.8, )
## If natural - what is the type type
table(reconciled$`4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`)##
## Other
## 1
## Polysaccharide based
## 10
## Polysaccharide based;dECM based
## 1
## Protein based
## 14
## Protein based;dECM based
## 8
## Protein based;Polysaccharide based
## 13
## Protein based;Polysaccharide based;dECM based
## 1
## Protein based;Polysaccharide based;Other
## 1
## Protein based;Polysaccharide based|Protein based;Polysaccharide based
## 1
## Protein based|dECM based
## 1
## Protein based|Protein based
## 1# output needs cleaning
table(reconciled$`4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`)##
## Other Pluronic
## 3 3
## Poly ethylene glycol (PEG)
## 3## 3rd level
table(reconciled$`4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`)##
## Collagens Collagens;Gelatin Collagens|Collagens
## 7 3 1
## Fibrinogen Fibrinogen;Other Gelatin
## 1 1 21
## Gelatin;Fibrinogen Gelatin;Other Gelatin;Silk-fibroin
## 1 2 1
## Gelatin|Gelatin Other
## 1 1table(reconciled$`4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`) ##
## Agarose Alginates
## 1 19
## Alginates;Other Chitosan
## 4 1
## Hyaluronic acid Hyaluronic acid|Hyaluronic acid
## 1 1## Origin of BioInk
t8 <- table(reconciled$`4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`)
# needs cleaning
# commercial (ready-to-use) custom formulated
# 6 44
# custom formulated|custom formulated Not reported
# 8 2
# Unclear Unclear|Unclear
# 2 1
# barplot(table(reconciled$`4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`), ylab = "Number of studies", cex.names=.5, col = "blue")
text(x = barplot(t8, ylab = "Number of studies", ylim=c(0, max(t8) + 10), cex.names=.5 , col = "blue", main = "What is the origin of BioInk"), y = t8 + 2, labels = t8, cex = 0.8, )
### Additives
# -- needs cleaning
table(reconciled$`4.1.3.3 Which information is provided on the additives in the ink or culture? _4c2b5908-9c3c-4ba8-91f2-dd9771ac2ad4_Answer`)##
## Concentration
## 9
## Concentration;Manufacturer
## 25
## Concentration;Manufacturer;Order number
## 8
## Concentration;Manufacturer;Order number|Concentration;Manufacturer;Order number
## 2
## Concentration;Manufacturer|Concentration;Manufacturer
## 4
## Concentration|Concentration;Manufacturer
## 1
## Manufacturer
## 1
## None
## 11
## None|Concentration;Manufacturer
## 1
## None|None
## 1# needs cleaning
## Cell density of bioInk
table(reconciled$`4.1.3.4 Is the cell density of the bioink provided in the study? _d03a497a-c8ab-49a6-8fa7-5eca0d168c94_Answer`)##
## No No|No No|Yes Yes Yes|No Yes|Yes
## 11 1 1 43 1 6# needs cleaning
# No No|No No|Yes Yes Yes|No Yes|Yes
# 11 1 1 43 1 6 BioInk Sunburst plot
#### BIOINK sunburst
# type -->
bioink <- reconciled %>%select(
study_ID,
`4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`,
`4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`,
`4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`,
`4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)
bioink <- bioink %>% rename(typeGeneral_level1 = `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`,
typeNatural_level2 = `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`,
typeSynthetic_level2 = `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`,
typeProtein_level3 = `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`,
typePoly_level3 = `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)
bioink_split1 <- separate_rows(bioink, typeGeneral_level1 , sep="\\|")
bioink_split1_1 <- separate_rows(bioink_split1, typeNatural_level2 , sep="\\|")
bioink_split1_2 <- separate_rows(bioink_split1_1, typeProtein_level3 , sep="\\|")
bioink_split1_3 <- separate_rows(bioink_split1_2, typePoly_level3 , sep="\\|")
bioink_split2 <- separate_rows(bioink_split1_3, typeNatural_level2 , sep=";")
bioink_split3 <- separate_rows(bioink_split2, typeProtein_level3 , sep=";")
bioink_split3 <- separate_rows(bioink_split3, typePoly_level3 , sep=";")
# 148 rows
# 164 rows
test_bio <- bioink_split3 %>%
mutate(typeNatural_level2 = replace(typeNatural_level2, typeGeneral_level1!="Natural", NA),
typeSynthetic_level2 = replace(typeSynthetic_level2, typeGeneral_level1!="Synthetic", NA),
typeProtein_level3 = replace(typeProtein_level3, typeNatural_level2!="Protein based", NA),
typePoly_level3 = replace(typePoly_level3, typeNatural_level2!="Polysaccharide based", NA)
)
test_bio$typeSynthetic_level2 <- as.character(test_bio$typeSynthetic_level2)
### merge level 2 & merge level 3
test_bio$level3 <- ifelse(!is.na(test_bio$typeProtein_level3), test_bio$typeProtein_level3, test_bio$typePoly_level3)
test_bio$level2 <- ifelse(!is.na(test_bio$typeNatural_level2), test_bio$typeNatural_level2, test_bio$typeSynthetic_level2)
#### START PLOTTING
library(dplyr)
library(plotme)
bioInk_count <- count(test_bio,
typeGeneral_level1,
level2,
level3
#,study_ID
)
# sunburst plot
count_to_sunburst(bioInk_count)# fill by group size
count_to_sunburst(bioInk_count, fill_by_n = TRUE)# treemap plot, ordered by group size
count_to_treemap(bioInk_count, sort_by_n = TRUE)Liver Cells
And then it is liver specific variables. - main type of liver cells - info about liver cells
AND - Main type of liver cells
if comments from reconciled - print out for all to see.
if any of reviewer comments say HepG2
table(reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`)##
## Hepatoma cells Hepatoma cells;Other
## 36 1
## Induced pluripotent stem cells Other
## 5 4
## Primary cells Primary cells;Other
## 16 1# clean? - Maren to clean
# Hepatoma cells Hepatoma cells;Other
# 28 2
# Induced pluripotent stem cells Induced pluripotent stem cells;Other
# 3 1
# Other Primary cells
# 12 14
# Primary cells;Hepatoma cells Primary cells;Other
# 2 1
### combine the ones with semi-colon - into a new category called "Combination"
liver_cells <- reconciled %>%
mutate(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer` = recode(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`, "Hepatoma cells;Other" = "Combination")) %>%
mutate(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer` = recode(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`, "Primary cells;Other" = "Combination"))
t8 <- table(liver_cells$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`)
#barplot(table(reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`), ylab = "Number of studies", cex.names=.5, col = "turquoise")
text(x = barplot(t8, ylab = "Number of studies", ylim=c(0, max(t8) + 10), cex.names=.5 , col = "turquoise", main = "What type of Liver Cells are included"), y = t8 + 2, labels = t8, cex = 0.8, )
# ABB bring in the "other" from comments box
###############################################################
# liver cells
#########################################
table(reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Comments`)##
## adult stem cell derived hepatocytes
## 2
## AML12
## 1
## bone marrow mesenchymal cells
## 1
## cholangiocarinoma cells
## 1
## Hep3B
## 2
## Hep3B\n
## 1
## HepaRG
## 3
## HepaRG;LX2
## 1
## hepatic progenitor cells
## 1
## Hepatocellular carcinoma
## 1
## HepG2
## 26
## hESC lines RC-6 and RC-10
## 1
## hiHep\n
## 1
## HMCS1SA
## 1
## Huh7
## 1
## Huh7;HepaRG
## 1
## human hepatocytes
## 5
## human hepatocytes; hepatic stellate cells
## 1
## human hepatocytes; LX2; aHSC
## 1
## Human iPSC-derived hepatocytes
## 1
## liver biopsies
## 1
## miHeps (mouse hepatocyte-like cells)
## 1
## murine hepatocytes
## 3
## murine hepatocytes\n
## 1
## porcine liver tissue
## 1## clean the type of liver cells comments
liverType <- tibble(
study_ID = reconciled$study_ID,
liverCells = reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`,
liverCellsComment = reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Comments`
)
# remove line breaks
library(stringr)
liverType$liverCellsComment <- str_replace_all(liverType$liverCellsComment, "[\n]" , "")
liverType <- separate_rows(liverType, liverCellsComment, sep=";")
# remove whitespace from start of str
liverType$liverCellsComment <- str_trim(liverType$liverCellsComment, "left")
# remove whitespace from end of str
liverType$liverCellsComment <- str_trim(liverType$liverCellsComment, "right")
liverType <- liverType %>%
mutate(liverCellsComment = recode(liverCellsComment, "Human iPSC-derived hepatocytes" = "human hepatocytes")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "adult stem cell derived hepatocytes" = "human hepatocytes")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "cholangiocarinoma cells" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "bone marrow mesenchymal cells" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "hepatic progenitor cells" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "hepatic stellate cells" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "Hepatocellular carcinoma" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "aHSC" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "AML12" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "hiHep" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "hESC lines RC-6 and RC-10" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "HMCS1SA" = "human cells - other")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "liver biopsies" = "human liver tissue")) %>%
mutate(liverCellsComment = recode(liverCellsComment, "porcine liver tissue" = "porcine liver tissue"))
# liverType$liverCellsComment
## group the mouse ones together
#
# create categories
liverType$liverCellsComment <- as.factor(liverType$liverCellsComment)
commentsLiver <- liverType %>% group_by(liverCellsComment) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
#install.packages("formattable")
library(formattable)
formattable(commentsLiver,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("turquoise")
))| liverCellsComment | n_unique |
|---|---|
| HepG2 | 26 |
| human cells - other | 10 |
| human hepatocytes | 10 |
| HepaRG | 5 |
| murine hepatocytes | 4 |
| Hep3B | 3 |
| NA | 3 |
| Huh7 | 2 |
| LX2 | 2 |
| human liver tissue | 1 |
| miHeps (mouse hepatocyte-like cells) | 1 |
| porcine liver tissue | 1 |
#### how is LIVER model Cultered?
table(reconciled$`2.2.1 How is the presented liver model cultured?_b6e561ab-c358-47fa-b9db-61e363b73223_Answer`)##
## Co-culture Monoculture Unclear
## 32 30 1 # Co-culture Monoculture Unclear
# 33 29 1
barplot(table(reconciled$`2.2.1 How is the presented liver model cultured?_b6e561ab-c358-47fa-b9db-61e363b73223_Answer`), ylab = "Number of studies", cex.names=.7, col = "turquoise")
# If co-culter - what type of non-parenchymal cells
# - need to clean
table(reconciled$`2.2.1.1 If co-cultured, what type of non-parenchymal cells are included?_b729a62f-2dc7-47b6-ada7-e1ed8e363918_Answer`)##
## Endothelial cells
## 7
## Endothelial cells;Hepatic stellate cells
## 5
## Endothelial cells;Other
## 6
## Hepatic stellate cells
## 2
## Immune cells;Endothelial cells;Hepatic stellate cells
## 1
## Immune cells;Hepatic stellate cells
## 2
## Other
## 10# Maren to clean manually - bring in "other" responses from comments box
# LIVER Model
table(reconciled$`3.1 Does the study present a vascularization of the model?_3b31a6ff-4233-4f24-9e7e-2d2eb3c83420_Answer`)##
## No Yes, with perfusion Yes, without perfusion
## 52 5 6 # No Yes, with perfusion Yes, without perfusion
# 52 5 6
barplot(table(reconciled$`3.1 Does the study present a vascularization of the model?_3b31a6ff-4233-4f24-9e7e-2d2eb3c83420_Answer`), ylab = "Number of studies", cex.names=.7, col = "turquoise")
## hypoxia
table(reconciled$`3.2 Do the authors address hypoxia/normoxia/oxygenation of the liver model?_5b050ea7-da29-4a47-bdea-23edda5877f2_Answer`)##
## No Yes, by measurement Yes, descriptive
## 59 1 3 # No Yes, by measurement Yes, descriptive
# 59 1 3
barplot(table(reconciled$`3.2 Do the authors address hypoxia/normoxia/oxygenation of the liver model?_5b050ea7-da29-4a47-bdea-23edda5877f2_Answer`), ylab = "Number of studies", cex.names=.7, col = "turquoise")
Liver Cell Meta-Data
# Meta-data for Liver cells
table(reconciled$`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer`)##
## Age Common cell line Common cell line;None
## 2 33 4
## Health status None Sex;Age
## 1 16 2
## Sex;Age;Common cell line Sex;Age;Health status
## 2 3## common cell line; None should get merged with Common Cell line option
# re-shape data to can number of bits of info?
liverCell_meta <- reconciled %>% mutate(`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer` = recode(`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer`, "Common cell line;None" = "Common cell line"))
liverMetaData <- separate_rows(liverCell_meta, `2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer` ,sep=";")
liverMetaData_heat <- liverMetaData[,c(1, 58)]
colnames(liverMetaData_heat) <- c("study_ID", "metaData")
countsMetaDat <- liverMetaData_heat %>% group_by(study_ID) %>% summarize(n_unique = length(unique(metaData)))
table(countsMetaDat$n_unique)##
## 1 2 3
## 56 2 5# 1 2 3
# 56 2 5
hist(countsMetaDat$n_unique, xlab = "Number of Meta-Data Items Reported", ylab = "Number of Papers", main = "Meta-Data Items about the Cell Lines Peformed per Paper", breaks = 3)
Cells
What types of included cells? - human, animal, both
table(reconciled$`2.1 What is the origin of the cells in the liver model?_d2a55b1d-f869-4fa4-83a1-94eeb126c6fb_Answer`)##
## Animal Both Human
## 4 14 45#
# Animal Both Human
# 4 14 45
barplot(table(reconciled$`2.1 What is the origin of the cells in the liver model?_d2a55b1d-f869-4fa4-83a1-94eeb126c6fb_Answer`), ylab = "Number of studies", cex.names=.7, col = "pink")
If human, is it Xeno-free?
table(reconciled$`2.1.1 If human, is the described liver model xeno-free/animal-free?_7b2396a2-218d-462b-b71f-0c68bc5e9911_Answer`)##
## No Unclear
## 43 2barplot(table(reconciled$`2.1.1 If human, is the described liver model xeno-free/animal-free?_7b2396a2-218d-462b-b71f-0c68bc5e9911_Answer`)
, ylab = "Number of studies", cex.names=.7, col = "pink")
Culture Conditions
What type of culture conditions? Liver Markers- what measurements?
table(reconciled$`5.2 How long were the liver models cultured after printing?_1f136f0a-786b-4e17-a97d-e5cf32f47b26_Answer`)##
## < 72 hours 2 weeks - 3 months 3 days - 2 weeks not reported
## 2 16 42 3 # < 72 hours 2 weeks - 3 months 3 days - 2 weeks not reported
# 2 16 42 3
barplot(table(reconciled$`5.2 How long were the liver models cultured after printing?_1f136f0a-786b-4e17-a97d-e5cf32f47b26_Answer`), ylab = "Number of studies", cex.names=.5, col = "purple")
## ABB to order these in time order Liver Markers
# WHICH
t <- table(reconciled$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Answer`)
# clean?
liverMarkers <- separate_rows(reconciled, `6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Answer` ,sep=";")
markers_heat <- liverMarkers[,c(1, 94)]
colnames(markers_heat) <- c("study_ID", "marker")
new_marker <- markers_heat %>% group_by(marker) %>% summarize(n_unique = length(unique(study_ID)))
new_marker$n_unique <- as.numeric(new_marker$n_unique)
sort_new_marker <- new_marker %>% arrange(desc(n_unique))
#install.packages("formattable")
library(formattable)
formattable(sort_new_marker,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| marker | n_unique |
|---|---|
| 06ef33d5-b5e2-4488-b9fe-ab75c0e0a610 | 1 |
| 093a312d-88cc-422b-97c9-b4599d72e8d8 | 1 |
| 0fa47e58-c59a-4408-aac3-5d8fa2c75586 | 1 |
| 1265e638-0000-418e-a05e-08223f8a99ff | 1 |
| 159da379-406f-4792-8160-c76769f0808a | 1 |
| 19341742-a14e-401c-b18f-1877c9c3b1b9 | 1 |
| 1f7c632f-5591-4864-8b1f-71325c2d812f | 1 |
| 22b46028-79ef-4448-aae6-bad900ce6008 | 1 |
| 22d8d41c-027a-4cf8-8ecc-2c6c4dd5a234 | 1 |
| 23513e70-fcf9-4b6b-ac65-44fed53ad8f2 | 1 |
| 23ef3667-cb91-4a90-9965-a529a2c21403 | 1 |
| 291a3569-048c-40d8-bf60-df092b633a4f | 1 |
| 2c304b0e-c1ab-4c33-b72a-150b6aae6784 | 1 |
| 2d0bc1f5-1dab-402e-95be-391183a64171 | 1 |
| 2ee0fea0-5568-463c-9941-f9f92e5b408a | 1 |
| 35f38190-148e-4859-a010-7982eaf2eec2 | 1 |
| 3b20736b-3ccc-408b-a869-bed9e12fcf6e | 1 |
| 456bf91c-3191-4bbc-8c58-d3c0ff918d8c | 1 |
| 49b67f4a-7bc8-43ef-b7fe-8447c1418bb7 | 1 |
| 508c21bb-80f6-4a15-a5cf-cbfa3c69032d | 1 |
| 54d4022f-0f82-470c-bde5-edd6e17e9df9 | 1 |
| 57d801a4-01e6-4926-94db-3e0a266e8c28 | 1 |
| 60af380e-434c-4cda-8622-401cddd00dde | 1 |
| 60f61788-ddbd-470c-8c10-e9d7a0c28b49 | 1 |
| 6357589b-6ef3-4f4e-85e3-08480a5fb795 | 1 |
| 691dc367-254a-48a7-9a40-3a349f7476c8 | 1 |
| 6f47955c-4164-444e-95f4-ea02866b5397 | 1 |
| 734144b9-42d1-47eb-83ed-afb7c7ad5827 | 1 |
| 7401d1cc-59ff-4859-afae-a28065132375 | 1 |
| 7ac74bf0-81b2-4662-af82-ac26e9d008b3 | 1 |
| 7c5736c9-a2cc-40d5-a4c4-722760c6b1d1 | 1 |
| 7f8919f1-36f3-4cd1-915a-b22a2c036b4f | 1 |
| 82c9eae7-3802-47f0-bd88-f6827a1e81c4 | 1 |
| 84922b7a-69ec-4801-a5e1-3e757eafaad3 | 1 |
| 8879436f-be33-4bde-95b9-e7016101123f | 1 |
| 8913f706-6a47-4fca-9ab4-5e69e93d12a1 | 1 |
| 89eeef77-49f2-44ef-ae3a-695e1ba36241 | 1 |
| 8aa93971-914e-4786-93dd-3c014d392b30 | 1 |
| 8aef8a73-c767-4ad6-a478-eebb1daa3201 | 1 |
| 8bc9b574-8465-4e0a-a07f-b609eab8d30a | 1 |
| 93cf9c26-e608-43c4-b1df-178bdf00a23f | 1 |
| 96ded665-2a27-40a4-864d-0bd60bd7cfba | 1 |
| a6e69fee-62a4-4654-8f31-f2537b05e8a6 | 1 |
| a8b5617f-0a81-437a-ae05-1889f53b7a1f | 1 |
| a9f18f8f-23f1-44e1-9cab-cf51cbcdc90d | 1 |
| af0abb61-3b07-4b8d-b651-67881c7df72b | 1 |
| bd34b0d1-8a3d-4225-add7-add7caaf93c8 | 1 |
| bfac329b-9c92-49fa-a82e-e83f0101c9c3 | 1 |
| c14af8b6-e7d9-4baa-ac2c-949487d2f8d1 | 1 |
| c4fdee85-6300-413d-97f3-662b2efd632c | 1 |
| c6ac7eef-ce3e-4a08-adf3-8e20bb025141 | 1 |
| c84b9308-8737-4f6c-b40b-c9598403fc86 | 1 |
| cb3f5665-dd13-41e7-8ba2-6bd2ddc5ba26 | 1 |
| cba0dcca-7ee9-4921-844e-16ac01541e08 | 1 |
| cef68bc4-6006-42f8-a56d-177f17d9473f | 1 |
| d0017bf5-654d-40a7-803d-28b4390cd736 | 1 |
| d64637f4-db91-4a52-a05a-61b2e8a48e94 | 1 |
| d852f5db-f656-4a6c-9ebb-c02ec9a2537d | 1 |
| e069f69c-0c6e-4129-aeaa-8cc8508e90cc | 1 |
| e3daf1ba-1293-41e9-9b68-360c2419b428 | 1 |
| f00f39d9-0d92-4fcf-ae53-1378d2e00524 | 1 |
| f6de0c24-8fe2-43d9-be75-e15536009e76 | 1 |
| fad5fa41-6871-461f-8b26-bfcd79692031 | 1 |
######################
# liver marker "Other"
t <- table(reconciled$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Comments`)
## clean the type of liver cells comments
liverMarkerComments_rows <- separate_rows(reconciled, `6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Comments`
,sep=";")
liverMarkerComments <- tibble(
study_ID = liverMarkerComments_rows$study_ID,
liverMarker = liverMarkerComments_rows$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Answer`,
liverMarkerComment = liverMarkerComments_rows$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Comments`
)
# remove line breaks
library(stringr)
liverMarkerComments$liverMarkerComment <- str_replace_all(liverMarkerComments$liverMarkerComment, "[\n]" , "")
liverMarkerComments_rows <- separate_rows(liverMarkerComments,liverMarkerComment
,sep=",")
liverMarkerComments_rows <- separate_rows(liverMarkerComments_rows,liverMarkerComment
,sep=" and")
# # would replace all white space
# liverMarkerComments$liverMarkerComment <- str_replace_all(liverMarkerComments$liverMarkerComment, "[^\d]+" , "")
# remove line breaks
liverMarkerComments_rows$liverMarkerComment <- str_replace_all(liverMarkerComments_rows$liverMarkerComment, "[\n]" , "")
# remove whitespace from start of str
liverMarkerComments_rows$liverMarkerComment <- str_trim(liverMarkerComments_rows$liverMarkerComment, "left")
# remove whitespace from end of str
liverMarkerComments_rows$liverMarkerComment <- str_trim(liverMarkerComments_rows$liverMarkerComment, "right")
liverMarkerComments_clean <- liverMarkerComments_rows %>%
mutate(liverMarkerComment = recode(liverMarkerComment,
"albumin" = "Albumin",
"ALB" = "Albumin",
" ALB" = "Albumin",
" ALB" = "Albumin",
"albumin ELISA" = "Albumin",
"Albumin " = "Albumin",
"albumin (ALB)" = "Albumin",
"qPCR: albumin" = "Albumin",
" Albumin" = "Albumin",
"human albumin" = "Albumin",
"urea" = "Urea",
"urea " = "Urea",
" urea" = "Urea",
" Urea" = "Urea",
" transthyre- tin (TTR)" = "TTR",
" transthyretin (TTR)" = "TTR",
" transthyretin TTR" = "TTR",
" TTR" = "TTR",
"transthyretin TTR" = "TTR",
"transthyretin (TTR)" = "TTR",
"transthyre- tin (TTR)" = "TTR",
"transferrin" = "TTR",
"HNF4a" = "HNF4alpha",
" HNF4a" = "HNF4alpha",
" HFN4alpha" = "HNF4alpha",
" HNF4alpha" = "HNF4alpha",
"HFN4A" = "HNF4alpha",
"s HNF4a" = "HNF4alpha",
" HNF4alpha" = "HNF4alpha",
" HFN4alpha" = "HNF4alpha",
"hepatocyte nuclear factor 4α (HNF4A)" = "HNF4alpha",
"HFN4alpha" = "HNF4alpha",
" AFP" = "AFP",
" alpha-fetoprotein (AFP)" = "AFP",
" alpha-fetoprotein" = "AFP",
"alpha-fetoprotein levels (HCC marker)" = "AFP",
" α-fetoprotein (AFP)" = "AFP",
"alpha-fetoprotein" = "AFP",
"Alpha-fetoprotein AFP" = "AFP",
"alpha-fetoprotein (AFP)" = "AFP",
"α-fetoprotein (AFP)" = "AFP",
" cytokeratin 19 (CK19)" = "cytokeratin 19 (CK19)",
" cytokeratin 19" = "cytokeratin 19 (CK19)",
" CK-19" = "cytokeratin 19 (CK19)",
"CK-19" = "cytokeratin 19 (CK19)",
"cytokeratin 19" = "cytokeratin 19 (CK19)",
"CK18" = "cytokeratin 18 (CK18)",
"staning for MRP2" = "MRP2",
" MRP2" = "MRP2",
"multidrug resist- ance-associated protein 2 (MRP2)" = "MRP2",
"CYP4A4 activity" = "CYP3A4",
" CYP3A4" = "CYP3A4",
" CYP3A4" = "CYP3A4",
"Cyp3A4"= "CYP3A4",
"cytochrome P450 3A4 (CYP3A4)" = "CYP3A4",
"Cytochromes 1A2" = "CYP1A2",
"alpha-1 antitrypsin (A1AT)" = "alpha-1 antitrypsin",
"alpha-1 antitrypsin (AAT)" = "alpha-1 antitrypsin",
"Alpha 1 antitrypsin" = "alpha-1 antitrypsin"
))
commentsLiver <- liverMarkerComments_clean %>% group_by(liverMarkerComment) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
commentsLiver#install.packages("formattable")
library(formattable)
formattable(commentsLiver,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| liverMarkerComment | n_unique |
|---|---|
| Albumin | 36 |
| NA | 23 |
| Urea | 16 |
| AFP | 12 |
| HNF4alpha | 9 |
| CYP3A4 | 7 |
| TTR | 5 |
| CYP1A2 | 4 |
| MRP2 | 4 |
| alpha-1 antitrypsin | 4 |
| 3 | |
| cytokeratin 18 (CK18) | 3 |
| cytokeratin 19 (CK19) | 3 |
| ASGR1 | 2 |
| CD31 | 2 |
| CYP | 2 |
| CYP2B6 | 2 |
| CYP2C19 | 2 |
| CYP2C9 | 2 |
| 2C19 | 1 |
| 3-dioxygenase | 1 |
| 3A4 | 1 |
| ABCC2 | 1 |
| ACTA2 | 1 |
| ALDOB | 1 |
| ASGPR1 | 1 |
| ATP | 1 |
| COLA1 | 1 |
| CYP1A1 | 1 |
| CYP2E1 | 1 |
| Collagen A1 | 1 |
| E-cadherin | 1 |
| Foxa3 | 1 |
| GST | 1 |
| Glutathione S-transferase alpha 1 | 1 |
| MDR1 | 1 |
| MMP2 | 1 |
| NMDA receptor 1 iso- form NR1-2 variant | 1 |
| NR1/2(PXR) | 1 |
| NR1H4(FXR) | 1 |
| OCT | 1 |
| SERPINA1 | 1 |
| TIMP1 | 1 |
| Zo-1 | 1 |
| alpha-smooth muscle actin (α-SMA) | 1 |
| asialoglycoprotein receptor 1 | 1 |
| beta-Catenin | 1 |
| bile salt export pump (BSEP) | 1 |
| ceruloplasmin | 1 |
| glucose_x0002_6-phosphatase catalytic subunit (ABCG2) | 1 |
| glutamate dehydrogenase | 1 |
| glutathione s-transferase alpha (alpha-GST) | 1 |
| hepatic markers ATP-binding cassette super-family G member 2 (G6PC) | 1 |
| hepatocyte nuclear factor 1α (HNF1A) | 1 |
| hepatocyte nuclear factor 3β (HNF3B) | 1 |
| hepatocyte nuclear factor 6 (HNF6) | 1 |
| organic anion transporter protein 1B3 (OATP1B3) | 1 |
| secretion | 1 |
| total bile acids | 1 |
| total protein | 1 |
| tryptophan 2 | 1 |
| tyrosine amino-transferase | 1 |
## trying 6.4
# which metabolites were analysed
t <- table(reconciled$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`)
liverMetabolite <- separate_rows(reconciled, `6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`
,sep=";")
liverMetabolite$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments` <- str_replace_all(liverMetabolite$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`, "[\n]" , "")
liverMetabolite_comments <- liverMetabolite %>% select(c(study_ID, `6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`, `6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`))
commentsLiverMetabolite <- liverMetabolite_comments %>% group_by(`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
otherLiverMetabolite <- liverMetabolite_comments %>% group_by(`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
# print(otherLiverMetabolite)
#install.packages("formattable")
library(formattable)
formattable(commentsLiverMetabolite,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| 6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer | n_unique |
|---|---|
| Albumin | 47 |
| Urea | 24 |
| None | 15 |
| Other | 7 |
| Bile acid | 3 |
#### CLEAN THIS!!
formattable(otherLiverMetabolite,
align = c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| 6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments | n_unique |
|---|---|
| NA | 56 |
| ASS, involved in Urea cycle | 1 |
| Alpha-fetoprotein AFP; transthyretin TTR | 1 |
| Glucose | 1 |
| albumin, alpha-fetoprotein, MKI67, Casp8 | 1 |
| alpha-fetoprotein, alpha-1-antitrypsin | 1 |
| aspartate, formate, glycine, histidine and tryptophan | 1 |
| transferrin, ceruloplasmin, alpha-1 antitrypsin | 1 |
#####################
# WHERE
table(reconciled$`6.1.1 Where were the liver markers measured?_a70225ae-f529-4b91-bcc9-8081d7efc6e2_Answer`)##
## Cells Media Media;Cells Not reported
## 10 15 19 19 # Cells Media Media;Cells Not reported
# 10 15 19 19
barplot(table(reconciled$`6.1.1 Where were the liver markers measured?_a70225ae-f529-4b91-bcc9-8081d7efc6e2_Answer`), ylab = "Number of studies", cex.names=.7, col = "yellow")
# CYP450
table(reconciled$`6.2 Does the study analyse the Cytochrome P450 CYP450 level in the print?_3c21cc1f-1431-49c7-ad8f-bda83956828a_Answer`)##
## No Yes
## 33 30 # No Yes
# 33 30
barplot(table(reconciled$`6.2 Does the study analyse the Cytochrome P450 CYP450 level in the print?_3c21cc1f-1431-49c7-ad8f-bda83956828a_Answer`), ylab = "Number of studies", cex.names=.7, col = "yellow")
table(reconciled$`6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`)##
## CYP1A CYP1A;CYP1A2
## 3 1
## CYP1A;CYP2E CYP1A;CYP3A
## 1 4
## CYP1A;CYP3A;CYP2B CYP1A;CYP3A;CYP2B;CYP2C
## 1 1
## CYP1A;CYP3A;CYP2B;CYP2C;CYP2D CYP1A;CYP3A;CYP2B;CYP2C;CYP2E
## 1 1
## CYP1A;CYP3A;CYP2B;YP2C;CYP2D CYP1A;CYP3A;CYP2C;CYP2D
## 1 1
## CYP2C CYP2E
## 1 1
## CYP3A CYP3A;CYP2D
## 9 1
## CYP3A;CYP2D;CYP2E Not reported
## 1 2## output needs cleaning
liverCytochome <- separate_rows(reconciled, `6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`
,sep=";")
liverCytochome_only <- liverCytochome %>% select(c(study_ID, `6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`, `6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Comments`))
liverCytochome_only <- liverCytochome_only %>% group_by(`6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
formattable(liverCytochome_only,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| 6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer | n_unique |
|---|---|
| NA | 33 |
| CYP3A | 21 |
| CYP1A | 15 |
| CYP2B | 5 |
| CYP2C | 5 |
| CYP2D | 5 |
| CYP2E | 4 |
| Not reported | 2 |
| CYP1A2 | 1 |
| YP2C | 1 |
## agonists
table(reconciled$`6.3 Have agonists of the receptors for the inducibility of CYPs been applied?_ac9f38a8-8fe2-44de-a180-24fddd78fb59_Answer`)##
## No Yes
## 55 8 # No Yes
# 54 9
barplot(table(reconciled$`6.3 Have agonists of the receptors for the inducibility of CYPs been applied?_ac9f38a8-8fe2-44de-a180-24fddd78fb59_Answer`), ylab = "Number of studies", cex.names=.7, col = "yellow")
Quality
- Quality Assays
- storage conditions
- application
- time
Does the study describe quality assurance?
table(reconciled$`5.3 Does the study describe quality-assuring assays for the printed model?_09745b80-9723-4e5c-8a47-dfc8a9c71a6d_Answer`)##
## Yes
## 63# Yes
# 63Which assays were performed?
library(tidyverse)
### messy - needs cleaning
messy_assay <- table(reconciled$`5.3.1 Which assays were performed to assure the quality of the liver model?_8a415412-28c6-48ff-840a-538ea69a068f_Answer`)
# needs cleaning
assays <- separate_rows(reconciled, `5.3.1 Which assays were performed to assure the quality of the liver model?_8a415412-28c6-48ff-840a-538ea69a068f_Answer` ,sep=";")
assays_heat <- assays[,c(1, 91)]
colnames(assays_heat) <- c("study_ID", "assay")
new <- assays_heat %>% group_by(assay) %>% summarize(n_unique = length(unique(study_ID)))
new$n_unique <- as.numeric(new$n_unique)
sort_new <- new %>% arrange(desc(n_unique))
#install.packages("formattable")
library(formattable)
formattable(sort_new,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("lightgreen")
))| assay | n_unique |
|---|---|
| 00cc0c37-b022-404a-b30e-d3fb8aae4eb2 | 1 |
| 023ca941-3fce-4c49-8775-12b6ffd16893 | 1 |
| 071c4d52-f350-4ad4-83f7-ea1e5ce2f4da | 1 |
| 080e5c5a-8808-4c2a-88dc-aeb5c0bf936c | 1 |
| 08d2b95b-de1b-4a0a-b35b-a0750f9173cd | 1 |
| 09029872-0c13-4234-958f-003758870e4a | 1 |
| 0a516490-66e5-46e7-8b0d-d6633c5e711f | 1 |
| 0fe1c7dd-cbba-4269-9c99-f4fbae0be8a2 | 1 |
| 10af8297-1353-4d84-b174-cdbf8e91f09d | 1 |
| 1708bd0c-bbf4-460b-a1d8-ac0bcf034747 | 1 |
| 20725176-9637-4eb5-9c26-268c8a9c7dd4 | 1 |
| 22d806c3-dd9f-4c0a-9f90-90a30de88143 | 1 |
| 23994736-df6e-4e3d-9bf6-cf72886473bc | 1 |
| 278ca74b-af55-4a9a-8109-28bc6daa16b8 | 1 |
| 27db00e3-1178-479f-b1ef-a6413bc44544 | 1 |
| 28f9b386-7074-4cbd-97cc-e61c24f8f418 | 1 |
| 2995b7a0-2470-48a6-bf6c-f0d340e72c35 | 1 |
| 2b4864eb-922f-4144-b57a-da58b5be3837 | 1 |
| 357dffe5-15fc-4b6e-834d-689d0df05f49 | 1 |
| 36373b30-b997-4d10-a7b0-614b35f5931d | 1 |
| 3905142b-ae1a-457a-87e2-eee65a7220e5 | 1 |
| 3b3aa440-092e-448d-9445-adb5ef1a632e | 1 |
| 3be15011-921d-428c-9dfb-16d8abd5c551 | 1 |
| 40120d28-f32c-44aa-9a0a-854ebe59831c | 1 |
| 42d83e6c-22da-4594-a9cd-433937f0f463 | 1 |
| 46f620d3-fccb-41a2-910b-a4a85546d6e1 | 1 |
| 473c388f-ba72-4b33-9371-efbc90268911 | 1 |
| 508fa86e-2248-4205-8250-285e0d9c5966 | 1 |
| 55799bab-9b0a-4005-a890-58f20cb065aa | 1 |
| 63f9ba31-13d3-46d6-87e9-51166eabeaf5 | 1 |
| 6a2da839-e5ce-4f42-bf65-72f9bb748462 | 1 |
| 731db6a7-9cfd-490f-989a-15bffea7a36a | 1 |
| 78c2d6f8-9141-45aa-9744-e5e1f8544c52 | 1 |
| 7bbcad4e-6f5a-408c-bd86-aa7658baa33f | 1 |
| 7c3eec45-6d93-4b2c-bb61-9be9107a5e38 | 1 |
| 7cbf4b0b-59dd-4e68-a7bf-28c7c7c41f0f | 1 |
| 803f0bb1-dc54-4d9a-84d5-73e255d24990 | 1 |
| 81be1c7b-18df-4bd4-b4df-3093f0a7e793 | 1 |
| 868ec33f-85c2-4129-a4c4-9ace595336d0 | 1 |
| 8777f4d4-f095-4bad-a4ee-7c32dfd0eae2 | 1 |
| 90fb9b24-7f8e-47b9-a100-2f9812329d4a | 1 |
| 991638e8-dac7-402d-b406-c8a09b0bb7de | 1 |
| 993e4e97-78b0-49f3-8648-4bd46e8cf4a9 | 1 |
| 9c4fdebe-6b99-4053-85a9-f6cef5c76676 | 1 |
| 9d79df0c-bf7c-4a29-aa09-1ad0897f4df6 | 1 |
| 9ed1d4cf-eae9-4790-8395-fba74c0f5f89 | 1 |
| b22d5793-bc9f-4cfb-b243-137ab23d21f1 | 1 |
| b2c5c748-8537-40f1-bb0a-bf5e5da87ef4 | 1 |
| b39d2dd2-9cac-44d7-9e05-9488a441131a | 1 |
| bea587d7-73e6-4ef3-b21a-9b847cdee87c | 1 |
| c7bef60d-a5d3-4cba-a6fa-d5267dd65d9e | 1 |
| c942ead2-9e10-455f-a210-8779bc3a9fd0 | 1 |
| c9d09bf3-980c-44eb-959b-767c60854b46 | 1 |
| cf37774b-2eda-4b55-a805-b0c6472d16aa | 1 |
| d2d4c81a-e355-44fa-8b68-9ebb3eef6e7e | 1 |
| d6b90343-0018-4635-b812-1c3f34b23791 | 1 |
| d7424460-0980-461c-8956-9d5c75639188 | 1 |
| d8f51651-3e50-46e6-bcba-1e03b8d20876 | 1 |
| ead21089-74c1-4ba3-ac93-6362ed3b1f2f | 1 |
| eb06ad61-f33b-4017-be14-6ef22d7363fa | 1 |
| f460aa28-3a25-4cc0-8706-ba797e835387 | 1 |
| f72292c9-2377-40ea-acd6-2e78ca48d045 | 1 |
| fb7fca9b-c897-461c-b807-a72d61137272 | 1 |
### 10 different tests are used across 63 papers, a total of 252 tests were used.
# the most popular are listed below
## also how many assay were performed? 1, 2, 3, 4, 5, or more.
counts <- assays_heat %>% group_by(study_ID) %>% summarize(n_unique = length(unique(assay)))
table(counts$n_unique)##
## 1
## 63 # 1 2 3 4 5 6 7
# 4 10 6 17 16 8 2
hist(counts$n_unique, xlab = "Number of Assays Reported", ylab = "Number of Papers", main = "Number of Assays Peformed per Paper",)
Applications in the paper
# Application
table(reconciled$`7.1 Do the authors apply the model in the study?_a6895d38-a9ed-4605-a092-4f06ebbd9e2b_Answer`)##
## No Yes
## 35 28 # No Yes
# 35 28
barplot(table(reconciled$`7.1 Do the authors apply the model in the study?_a6895d38-a9ed-4605-a092-4f06ebbd9e2b_Answer`), ylab = "Number of studies", cex.names=.7, col = "gray")
# field of application
table(reconciled$`7.1.1 please select the field of application. _d332c2ee-9864-434f-8595-90305808e32d_Answer`)##
## Disease modeling
## 3
## Drug dosage testing
## 2
## Drug dosage testing;Disease modeling
## 1
## Drug dosage testing;Xenograft (implantation into animal);Disease modeling
## 1
## Implant / Medical surgery
## 1
## Other
## 3
## Toxicity testing
## 10
## Toxicity testing;Drug dosage testing
## 4
## Toxicity testing;Drug dosage testing;Other
## 1
## Xenograft (implantation into animal)
## 2# import numpy as np
# import pandas as pd
#
# import plotly.graph_objects as go
# import plotly.express as px
# import plotly.colors
# from plotly.subplots import make_subplots
#
# from rpy2.robjects.packages import importr
# import rpy2.robjects
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:formattable':
##
## style## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutscreening_info <- c('Records obtained from the Medline database',
'Records obtained from previous reviews',
"",
"Exclusion critera:<br>
- work relying only on MRI;<br>
- work relying only on histology or equivalent approach;<br>
- work reporting only qualitative comparisons.",
"",
'Records selected for full-text evaluation',
"",
"Exclusion criteria:<br>
- studies using MRI-based measures in arbitrary units;<br>
- studies using measures of variation in myelin content;<br>
- studies using arbitrary assessment scales;<br>
- studies comparing absolute measures of myelin with relative measures;<br>
- studies reporting other quantitative measures than correlation or R^2 values;<br>
- studies comparing histology from one dataset and MRI from a different one.",
"",
'Studies selected for literature overview',
"",
"Exclusion criteria:<br>
- not providing an indication of both number of subjects and number of ROIs.",
"")
fig1 <- plot_ly(
type = "sankey",
orientation = "h",
# arrangement = "freeform",
node = list(
label = c("Main records identified (database searching)",
"Additional records (reviews)",
"Records screened",
"Records excluded",
"Full-text articles assessed for eligibility",
"Full-text articles excluded",
"Studied included in the literature overview",
"Studies included in the meta-analysis"),
Color = c("darkblue","darkblue","darkblue","darkred","darkgreen","darkred","darkgreen","darkgreen"),
pad = 15,
thickness = 20,
line = list(
color = "black",
width = 0.5
),
domain =
list(x = c(0, 0, 0.4, 0.6, 0.5, 0.8, 0.7, 1),
y = c(0, 0, 0.5, 0.8, 0.15, 0.05, 0.4, 0.6)),
link = list(
source = c(0, 1, 2, 2, 4, 4, 6),
target = c(2, 2, 3, 4, 5, 6, 7),
value = c(688, 1, 597, 92, 34, 58, 43)
# ,customdata = screening_info,
# hovertemplate = "%{customdata}"
),
width=950,
height=500
# x=1.02, y=-0.21,
# sizex=0.05, sizey=0.05,
# xanchor="right", yanchor="bottom"
)
)
fig1 <- fig1 %>% layout(
title = "Screening Sankey Diagram",
font = list(
size = 12
)
)
fig1###########################################################
fig <- plot_ly(
type = "sankey",
orientation = "h",
node = list(
label = c("Main records identified (database searching)",
"Additional records (reviews)",
"Records screened",
"Records excluded",
"Full-text articles assessed for eligibility",
"Full-text articles excluded",
"Studied included in the literature overview",
"Studies included in the meta-analysis"),
Color = c("darkblue","darkblue","darkblue","darkred","darkgreen","darkred","darkgreen","darkgreen"),
pad = 15,
thickness = 20,
line = list(
color = "black",
width = 0.5
)
),
link = list(
source = c(0, 1, 2, 2, 4, 4, 6),
target = c(2, 2, 3, 4, 5, 6, 7),
value = c(688, 1, 597, 92, 34, 58, 43)
)
)
fig <- fig %>% layout(
title = "Basic Sankey Diagram",
font = list(
size = 10
)
)
fig# fig1 = go.Figure(data=[go.Sankey(
# arrangement = "freeform",
# node = dict(
# pad = 15,
# thickness = 20,
# line = dict(color = "black", width = 0.5),
# # label = ["Main records identified (database searching)",
# # "Additional records (reviews)",
# # "Records screened",
# # "Records excluded",
# # "Full-text articles assessed for eligibility",
# # "Full-text articles excluded",
# # "Studied included in the literature overview",
# # "Studies included in the meta-analysis"],
# x = [0, 0, 0.4, 0.6, 0.5, 0.8, 0.7, 1],
# y = [0, 0, 0.5, 0.8, 0.15, 0.05, 0.4, 0.6],
# hovertemplate = "%{label}<extra>%{value}</extra>",
# # color = ["darkblue","darkblue","darkblue","darkred","darkgreen","darkred","darkgreen","darkgreen"]
# ),
# link = dict(
# source = [0, 1, 2, 2, 4, 4, 6],
# target = [2, 2, 3, 4, 5, 6, 7],
# value = [688, 1, 597, 92, 34, 58, 43],
# customdata = screening_info,
# hovertemplate = "%{customdata}",
# ))])
#
# fig1.update_layout(images=[dict(
# source="neurolibre.svg",
# xref="paper", yref="paper",
# x=1.02, y=-0.21,
# sizex=0.05, sizey=0.05,
# xanchor="right", yanchor="bottom"
# )],
# width=950,
# height=500,
# font_size=12)
#
# fig1.add_annotation(x=1.003, y=-0.205, xref="paper", yref="paper",
# text="<a href=\"https://neurolibre.com\" target=\"_blank\" " +
# "style=\"color:gray!important\">NeuroLibre</a>", showarrow=False)
#
# fig1.show()